Toward End-to-end Artificial Intelligence Systems for Radiographic Fracture Assessment: A Critical Review of Detection, Explainability, and Prognosis.
Authors
Affiliations (1)
Affiliations (1)
- Department of Computer Science and Engineering, Oriental University, Indore, Madhya Pradesh, India.
Abstract
This review synthesizes recent advances in fracture intelligence through a unified seven-stage end-to-end pipeline that includes preprocessing and (region of interest) ROI extraction, fracture detection, classification, localization and segmentation, prognostic modeling, explainability, and clinical deployment. By shifting the focus from isolated algorithms to complete clinical workflows, we expose a critical gap: current systems excel at basic detection but struggle with complex prognostic modeling and real-world deployment. Within this framework, we critically analyze methodological progress, highlight enduring research challenges, and demonstrate that fracture artificial intelligence continues to be detection focused with relatively less advancement in quantitative fracture characterization, prognostic intelligence, intrinsically interpretable learning, and prospective clinical validation. Based on these findings, we outline future research directions toward unified, anatomically informed, clinically deployable fracture intelligence systems.